Yoon Kim.2014.Convolutional neural networks for sentence classification.In Proceedings of the 2014 Confer- ence on Empirical Methods in Natural Language Processing,EMNLP 2014,A meeting of SIGDAT,a Special Interest Group of the ACL,pages 1746-1751. J Peter Kincaid,Robert P Fishburne Jr,Richard L Rogers,and Brad S Chissom.1975.Derivation of new read- ability formulas(automated readability index,fog count and flesch reading ease formula)for navy enlisted personnel.Technical report,Naval Air Station,Memphis,TN. Omer Levy and Yoav Goldberg.2014a.Dependency-based word embeddings.In Meeting of the Association for Computational Linguistics,pages 302-308. Omer Levy and Yoav Goldberg.2014b.Neural word embedding as implicit matrix factorization.In Advances in neural information processing systems,pages 2177-2185. Quan Liu,Hui Jiang,Si Wei,Zhen-Hua Ling,and Yu Hu.2015.Learning semantic word embeddings based on ordinal knowledge constraints.In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics,pages 1501-1511. Yi Ma,Eric Fosler-Lussier,and Robert Lofthus.2012.Ranking-based readability assessment for early primary children's literature.In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,pages 548-552. G Harry McLaughlin.1969.Smog grading:A new readability formula.Journal of reading,12(8):639-646. Tomas Mikolov,Ilya Sutskever,Kai Chen,Greg S Corrado,and Jeff Dean.2013.Distributed representations of words and phrases and their compositionality.In Advances in neural information processing systems,pages 3111-3119. Jeffrey Pennington,Richard Socher,and Christopher D.Manning.2014.Glove:Global vectors for word repre- sentation.In Empirical Methods in Natural Language Processing(EMNLP),pages 1532-1543. Bryan Perozzi,Rami Al-Rfou,and Steven Skiena.2014.Deepwalk:Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining pages701-710. Emily Pitler and Ani Nenkova.2008.Revisiting readability:A unified framework for predicting text quality.In Conference on Empirical Methods in Natural Language Processing,EMNLP 2008,A meeting of SIGDAT,a Special Interest Group of the ACL,pages 186-195. Yafeng Ren,Yue Zhang,Meishan Zhang,and Donghong Ji.2016.Improving twitter sentiment classification using topic-enriched multi-prototype word embeddings.In Thirtieth AAAI Conference on Artificial Intelligence,pages 3038-3044. Sam T.Roweis and Lawrence K.Saul.2000.Nonlinear dimensionality reduction by locally linear embedding. Science,290(5500):2323-6. Elliot Schumacher,Maxine Eskenazi,Gwen Frishkoff,and Kevyn Collins-Thompson.2016.Predicting the rela- tive difficulty of single sentences with and without surrounding context.In Conference on Empirical Methods in Natural Language Processing,pages 1871-1881. Sarah E Schwarm and Mari Ostendorf.2005.Reading level assessment using support vector machines and statistical language models.In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics,pages 523-530 Jie Shen and Cong Liu.2016.Improved word embeddings with implicit structure information.In COLING 2016, 26th International Conference on Computational Linguistics,Proceedings of the Conference,pages 2408-2417. Luo Si and James P.Callan.2001.A statistical model for scientific readability.In Proceedings of the 2001 ACM CIKM International Conference on Information and Knowledge Management,pages 574-576. Manjira Sinha,Tirthankar Dasgupta,and Anupam Basu.2014.Influence of target reader background and text fea- tures on text readability in bangla:A computational approach.In COLING 2014,25th International Conference on Computational Linguistics,Proceedings of the Conference,pages 345-354. Duyu Tang.Furu Wei,Nan Yang,Ming Zhou,Ting Liu,and Bing Qin.2014.Learning sentiment-specific word embedding for twitter sentiment classification.In Meeting of the Association for Computational Linguistics, pages1555-1565. 377377 Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Confer￾ence on Empirical Methods in Natural Language Processing, EMNLP 2014, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 1746–1751. J Peter Kincaid, Robert P Fishburne Jr, Richard L Rogers, and Brad S Chissom. 1975. Derivation of new read￾ability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. Technical report, Naval Air Station, Memphis, TN. Omer Levy and Yoav Goldberg. 2014a. Dependency-based word embeddings. In Meeting of the Association for Computational Linguistics, pages 302–308. Omer Levy and Yoav Goldberg. 2014b. Neural word embedding as implicit matrix factorization. In Advances in neural information processing systems, pages 2177–2185. Quan Liu, Hui Jiang, Si Wei, Zhen-Hua Ling, and Yu Hu. 2015. Learning semantic word embeddings based on ordinal knowledge constraints. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pages 1501–1511. Yi Ma, Eric Fosler-Lussier, and Robert Lofthus. 2012. Ranking-based readability assessment for early primary children’s literature. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 548–552. G Harry McLaughlin. 1969. Smog grading: A new readability formula. Journal of reading, 12(8):639–646. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111–3119. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word repre￾sentation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 701–710. Emily Pitler and Ani Nenkova. 2008. Revisiting readability: A unified framework for predicting text quality. In Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 186–195. Yafeng Ren, Yue Zhang, Meishan Zhang, and Donghong Ji. 2016. Improving twitter sentiment classification using topic-enriched multi-prototype word embeddings. In Thirtieth AAAI Conference on Artificial Intelligence, pages 3038–3044. Sam T. Roweis and Lawrence K. Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323–6. Elliot Schumacher, Maxine Eskenazi, Gwen Frishkoff, and Kevyn Collins-Thompson. 2016. Predicting the rela￾tive difficulty of single sentences with and without surrounding context. In Conference on Empirical Methods in Natural Language Processing, pages 1871–1881. Sarah E Schwarm and Mari Ostendorf. 2005. Reading level assessment using support vector machines and statistical language models. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 523–530. Jie Shen and Cong Liu. 2016. Improved word embeddings with implicit structure information. In COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference, pages 2408–2417. Luo Si and James P. Callan. 2001. A statistical model for scientific readability. In Proceedings of the 2001 ACM CIKM International Conference on Information and Knowledge Management, pages 574–576. Manjira Sinha, Tirthankar Dasgupta, and Anupam Basu. 2014. Influence of target reader background and text fea￾tures on text readability in bangla: A computational approach. In COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference, pages 345–354. Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu, and Bing Qin. 2014. Learning sentiment-specific word embedding for twitter sentiment classification. In Meeting of the Association for Computational Linguistics, pages 1555–1565